{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fitting neutrino oscillations with IceCube DeepCore\n",
    "***Bruno Benkel***, *PhD. student at DESY Zeuthen.*\n",
    "\n",
    "For details on the analysis please see [Measurement of atmospheric neutrino mixing with improved IceCube DeepCore calibration and data processing](https://journals.aps.org/prd/abstract/10.1103/PhysRevD.108.012014), published in Phys. Rev. D 108, 012014 (2023).\n",
    "\n",
    "Here we present a fit of MC to the data. The aim of this example is to show end users how to:\n",
    "* treat the MC and data;\n",
    "* compute flux using `daemonflux` (see the [publication](https://journals.aps.org/prd/abstract/10.1103/PhysRevD.108.012014) and [repository](https://github.com/mceq-project/daemonflux));\n",
    "* calculate neutrino oscillation parameters using a 2-flavor approximation; and\n",
    "* re-weight MC events using hypersurfaces, as presented in the analysis publication.\n",
    "\n",
    "Note that the fit is very simplistic and we ignore many systematic uncertainties, e.g. flux, cross-section, atmospheric muon scale, and normalization. Therefore, we do not expect this example to fully reproduce the paper result, but just to provide an easy-to-follow procedure for how to use the files in this release.\n",
    "\n",
    "This notebook was developed using Python version 3.10.2. Each package and its version are\n",
    "\n",
    "* `daemonflux`: 0.8.2.\n",
    "* `matplotlib`: 3.8.4.\n",
    "* `numpy`: 1.22.4.\n",
    "* `pandas`: 2.2.3.\n",
    "* `scipy`: 1.13.1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import daemonflux # Flux estimation.\n",
    "import numpy as np # Array handling.\n",
    "import pandas as pd # Data handling.\n",
    "from scipy import optimize # Fitting.\n",
    "import matplotlib.pyplot as plt # Plotting.\n",
    "from copy import deepcopy # Misc."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "First, we import the `.csv` files into pandas dataframes then setup binning and livetime from the publication."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the data files.\n",
    "PATH = \"./\"\n",
    "DSETS = [\n",
    "    \"data\",                                               # Data.\n",
    "    \"mc_nu_nc\", \"mc_nue_cc\", \"mc_numu_cc\", \"mc_nutau_cc\", # MC neutrinos.\n",
    "    \"mc_mu\",                                              # MC muons.\n",
    "    \"hs_nu_nc_nue_cc\", \"hs_numu_cc\", \"hs_nutau_cc\"        # Hypersurfaces.\n",
    "]\n",
    "dfs = {dataset: pd.read_csv(PATH + dataset + \".csv\") for dataset in DSETS}\n",
    "\n",
    "# Combine MC neutrino sets.\n",
    "dfs[\"mc_nu\"] = pd.DataFrame()\n",
    "for k, df in [(k, v) for k, v in dfs.items() if \"mc_nu\" in k and k != \"mc_nu\"]:\n",
    "    dfs[\"mc_nu\"] = pd.concat([dfs[\"mc_nu\"], df], axis=0, ignore_index=True)\n",
    "    del dfs[k]\n",
    "\n",
    "# Create quick references to dict entries.\n",
    "data, mcnu, mcmu = dfs[\"data\"], dfs[\"mc_nu\"], dfs[\"mc_mu\"]\n",
    "dhs = {k: df for k, df in dfs.items() if \"hs\" in k}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Setup binning from the readme.\n",
    "EN_MIN, EN_MAX = 6.31, 158.49\n",
    "CZ_MIN, CZ_MAX = -1., .1\n",
    "\n",
    "BINS = {\n",
    "    \"reco_energy\" : np.logspace(np.log10(EN_MIN), np.log10(EN_MAX), num=12),\n",
    "    \"reco_coszen\" : np.linspace(CZ_MIN, CZ_MAX, num=11),\n",
    "    \"pid\"         : np.array([0.55, 0.75, 1.00]),\n",
    "}\n",
    "\n",
    "# Last energy bin has double size to contain sufficient statistics.\n",
    "BINS[\"reco_energy\"] = np.delete(BINS[\"reco_energy\"], -2)\n",
    "\n",
    "# Get neutrino count from data.\n",
    "NUCOUNT = sum(data[\"count\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "Then, we estimate neutrino flux using `daemonflux` and prepare functions to estimate neutrino oscillation using the 2-flavor approximation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Downsize input MC data for expediency.\n",
    "SCALING_FACTOR = 10\n",
    "mcnu = mcnu.iloc[::SCALING_FACTOR, :].reset_index(drop=True)\n",
    "\n",
    "# Normalize weights after scaling.\n",
    "mcnu[\"weight\"] *= SCALING_FACTOR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Progress:\n",
      "     0 |  39685\n",
      " 10000 |  39685\n",
      " 20000 |  39685\n",
      " 30000 |  39685\n",
      "Done!\n"
     ]
    }
   ],
   "source": [
    "# Get flux using daemonflux (see https://github.com/mceq-project/daemonflux).\n",
    "FLAVOR_FLUX = (\"nue\", \"numu\")\n",
    "dflux = daemonflux.Flux(location = \"generic\")\n",
    "\n",
    "# Store nu flux in corresponding arrays.\n",
    "flux = {}\n",
    "for fl in FLAVOR_FLUX:\n",
    "    flux[fl] = np.zeros_like(mcnu[\"weight\"])\n",
    "\n",
    "print(\"Progress:\")\n",
    "for ei in range(len(mcnu)):\n",
    "    # Check if nu or antinu.\n",
    "    prefix = \"\"\n",
    "    if mcnu[\"pdg\"][ei] < 0: prefix = \"anti\"\n",
    "\n",
    "    # Get zenith angle in degrees, from -90 to 90.\n",
    "    zen = np.rad2deg(np.arccos(mcnu[\"true_coszen\"][ei]))\n",
    "    if zen > 90: zen = 180-zen\n",
    "\n",
    "    # Get flux.\n",
    "    for fl in FLAVOR_FLUX:\n",
    "        flux[fl][ei] = dflux.flux([mcnu[\"true_energy\"][ei]], zen, prefix+fl)\n",
    "\n",
    "    # Print progress.\n",
    "    if ei % 1e4 == 0: print(\"%6d | %6d\" % (ei, len(mcnu)))\n",
    "print(\"Done!\")\n",
    "\n",
    "# Correct flux -- daemonflux reports E^3 * flux in [GeV^-4 m^-2 s^-1 sr^-1], our\n",
    "#     weight is in [GeV cm^2 sr].\n",
    "for fl in FLAVOR_FLUX:\n",
    "    flux[fl] *= 1e4 # m^2 -> cm^2.\n",
    "    mcnu[fl + \"_flux\"] = flux[fl] / pow(mcnu[\"true_energy\"], 3) # GeV^-4 -> -1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Constants to associate zenith angle with distance.\n",
    "L1 = 19.\n",
    "R  = 6378.2 + L1\n",
    "def zen_to_d(coszen: float) -> float:\n",
    "    \"\"\"\n",
    "    Converts the cosine of the zenith angle into distance.\n",
    "\n",
    "    Args:\n",
    "        coszen  : cosine of the zenith angle of the event.\n",
    "        returns : event's distance in meters.\n",
    "    \"\"\"\n",
    "    zen = np.arccos(coszen)\n",
    "    phi = np.arcsin((1-L1/R) * np.sin(zen))\n",
    "    psi = zen - phi\n",
    "    return np.sqrt((R-L1)**2 + R**2 - (2*(R-L1) * R * np.cos(psi)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define functions to obtain oscillated flux, so that we can later fit.\n",
    "TAGS = {\n",
    "    \"nue\"   : mcnu[\"pdg\"] % 12 == 0,\n",
    "    \"numu\"  : mcnu[\"pdg\"] % 14 == 0,\n",
    "    \"nutau\" : mcnu[\"pdg\"] % 16 == 0,\n",
    "}\n",
    "\n",
    "def osc_flux(mcnu: pd.DataFrame, amp: float, dm31: float) -> list[float]:\n",
    "    \"\"\"\n",
    "    Computes neutrino transition probability using the 2-flavor approximation,\n",
    "    returning an oscillated flux array that takes muon neutrino disappearance\n",
    "    and tau neutrino appearance into account.\n",
    "\n",
    "    Args:\n",
    "        mcnu    : pandas dataframe with MC neutrinos.\n",
    "        amp     : real value for the sin2(2 * theta23) in the 2-flavor approx-\n",
    "                  imation.\n",
    "        dm31    : neutrino mass splitting in eV^2.\n",
    "        returns : array with the oscillated flux.\n",
    "    \"\"\"\n",
    "    baseline = zen_to_d(mcnu[\"true_coszen\"]) / mcnu[\"true_energy\"]\n",
    "    ptau = amp * np.sin(1.267 * dm31 * baseline)**2\n",
    "    pmu  = 1 - ptau\n",
    "\n",
    "    # Compute oscillated flux.\n",
    "    oflux = np.zeros_like(mcnu[\"weight\"])\n",
    "\n",
    "    oflux[TAGS[\"nue\"]]   = deepcopy(mcnu[\"nue_flux\"][TAGS[\"nue\"]])\n",
    "    oflux[TAGS[\"numu\"]]  = mcnu[\"numu_flux\"][TAGS[\"numu\"]] *pmu[TAGS[\"numu\"]]\n",
    "    oflux[TAGS[\"nutau\"]] = mcnu[\"numu_flux\"][TAGS[\"nutau\"]]*ptau[TAGS[\"nutau\"]]\n",
    "    \n",
    "    return oflux"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "Prepare functions to reweight the MC neutrino histogram. We also plot a histogram to show the effect of this reweighting for an example mass splitting value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Setup hypersurfaces parameters.\n",
    "NOMINAL_SYSTEMATICS = { # From readme.\n",
    "    \"dom_eff\"          :  1.00,\n",
    "    \"hole_ice_p0\"      :  0.10,\n",
    "    \"hole_ice_p1\"      : -0.05,\n",
    "    \"bulk_ice_abs\"     :  1.00,\n",
    "    \"bulk_ice_scatter\" :  1.00,\n",
    "}\n",
    "\n",
    "def get_corr_factors(hs: pd.DataFrame, fit: dict[str, float]) -> pd.Series:\n",
    "    \"\"\"\n",
    "    Computes the hypersurfaces correction factors for a given fit.\n",
    "\n",
    "    Args:\n",
    "        hs      : pandas dataframe with the binned hypersurfaces gradients.\n",
    "        fit     : detector systematics to be tested.\n",
    "        returns : array hypersurfaces correction for each bin.\n",
    "    \"\"\"\n",
    "    # Compute difference between fit and nominal.\n",
    "    diff = {k: v - NOMINAL_SYSTEMATICS[k] for k, v in fit.items()}\n",
    "    return hs[\"intercept\"] + sum([hs[k] * v for k, v in diff.items()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "HIST_SHAPE = tuple(map(lambda v: len(v)-1, [v for v in BINS.values()]))\n",
    "def make_mcnu_hist(\n",
    "    mcnu: pd.DataFrame, dhs: dict[str, pd.DataFrame] = None, dm31: float = None,\n",
    "    fit: dict[str, float] = None\n",
    ") -> np.ndarray[HIST_SHAPE, float]:\n",
    "    \"\"\"\n",
    "    Makes four histograms from MC neutrino dataframe (nu_nc, nue_cc, numu_cc,\n",
    "    nutau_cc), applies hypersurfaces corrections to each, and returns the sum of\n",
    "    each histogram.\n",
    "\n",
    "    Args:\n",
    "        mcnu    : pandas dataframe with the MC neutrinos.\n",
    "        dhs     : dictionary with hypersurface for each dm31 value.\n",
    "        dm31    : mass splitting value to be tested.\n",
    "        fit     : detector systematics to be tested.\n",
    "        returns : histogram with the corrected MC neutrinos.\n",
    "    \"\"\"\n",
    "\n",
    "    # Assert that dm31 is within the hypersurfaces interpolation range.\n",
    "    dm31bins = dhs[\"hs_numu_cc\"][\"deltam31\"].unique()\n",
    "    if dm31 < dm31bins.min() or dm31bins.max() < dm31:\n",
    "        raise ValueError(\"dm31 of %f is outside of interpolation range.\" % dm31)\n",
    "\n",
    "    # Get dm31 bin length.\n",
    "    binlen = dm31bins[1] - dm31bins[0]\n",
    "\n",
    "    # Separate MC nu dataframe.\n",
    "    mc = {\n",
    "        \"nu_nc\"    : mcnu[(mcnu[\"type\"] == 0)],\n",
    "        \"nue_cc\"   : mcnu[(mcnu[\"type\"] != 0) & (abs(mcnu[\"pdg\"]) == 12)],\n",
    "        \"numu_cc\"  : mcnu[(mcnu[\"type\"] != 0) & (abs(mcnu[\"pdg\"]) == 14)],\n",
    "        \"nutau_cc\" : mcnu[(mcnu[\"type\"] != 0) & (abs(mcnu[\"pdg\"]) == 16)],\n",
    "    }\n",
    "    # Prepare sum of histograms.\n",
    "    hist_total, _ = np.histogramdd([[], [], []], bins = list(BINS.values()))\n",
    "\n",
    "    # Iterate through neutrino flavors.\n",
    "    for flavor, df in mc.items():\n",
    "        # Make MC histogram.\n",
    "        hist, _ = np.histogramdd(\n",
    "            [df[\"reco_energy\"], df[\"reco_coszen\"], df[\"pid\"]],\n",
    "            weights = NUCOUNT * df[\"osc_flux\"] * df[\"weight\"],\n",
    "            bins = list(BINS.values())\n",
    "        )\n",
    "\n",
    "        # Apply hypersurfaces correction.\n",
    "        if all(par is not None for par in [dhs, dm31, fit]):\n",
    "            # Get the right HS entry.\n",
    "            hs = [df for key, df in dhs.items() if flavor in key][0]\n",
    "\n",
    "            # Get hypersurface below and above dm31.\n",
    "            hs_lower = hs.loc[\n",
    "                (dm31 - binlen < hs[\"deltam31\"]) & (hs[\"deltam31\"] < dm31)\n",
    "            ].reset_index()\n",
    "            hs_upper = hs.loc[\n",
    "                (dm31 < hs[\"deltam31\"]) & (hs[\"deltam31\"] < dm31 + binlen)\n",
    "            ].reset_index()\n",
    "\n",
    "            # Create the interpolated hypersurface.\n",
    "            hs_int = hs_lower.copy()\n",
    "            for p in fit.keys():\n",
    "                grad = (np.array(hs_upper[p]) - np.array(hs_lower[p])) / binlen\n",
    "                hs_int[p] = grad * (dm31 - hs_lower[\"deltam31\"][0]) + hs_lower[p]\n",
    "\n",
    "            # Compute the correction factors.\n",
    "            corr_factors = get_corr_factors(hs_int, fit)\n",
    "            \n",
    "            # Make the HS correction histogram.\n",
    "            hs_corr, _ = np.histogramdd(\n",
    "                [hs_int[\"reco_energy\"], hs_int[\"reco_coszen\"], hs_int[\"pid\"]],\n",
    "                weights = corr_factors, bins = list(BINS.values())\n",
    "            )\n",
    "\n",
    "            # Apply HS correction.\n",
    "            hist *= hs_corr\n",
    "\n",
    "        # Add corrected histogram to total.\n",
    "        hist_total += hist\n",
    "\n",
    "    # Return sum of MC neutrino histograms.\n",
    "    return hist_total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_hist(hist: np.ndarray[HIST_SHAPE, float], cmap: str = \"OrRd\"):\n",
    "    \"\"\"\n",
    "    Plot a 3D histogram, separating it into two 2D histograms by PID.\n",
    "\n",
    "    Args:\n",
    "        hist : histogram we should plot.\n",
    "        cmap : color map to use. Default is \"OrRd\".\n",
    "    \"\"\"\n",
    "    fig, axs = plt.subplots(ncols = 2, figsize = (9, 4), sharey = \"row\")\n",
    "\n",
    "    # Iterate through PID bins.\n",
    "    for pi in range(len(BINS[\"pid\"]) - 1):\n",
    "        # Draw the histogram.\n",
    "        phist = axs[pi].pcolormesh(\n",
    "            BINS[\"reco_energy\"], BINS[\"reco_coszen\"],\n",
    "            hist[:,:,pi].T, cmap=cmap, vmin=np.min(hist), vmax=np.max(hist)\n",
    "        )\n",
    "\n",
    "        # Setup axes and titles.\n",
    "        axs[pi].set_xscale(\"log\")\n",
    "        XTICKS = np.logspace(3, 7, num=5, base=2)\n",
    "        axs[pi].set_xticks(XTICKS, XTICKS.astype(int))\n",
    "        axs[pi].set_xlabel(r\"$E_\\text{reco}$ (GeV)\")\n",
    "        axs[pi].set_title(\"Mixed\" if BINS[\"pid\"][pi] == 0.55 else \"Tracks\")\n",
    "    axs[0].set_ylabel(r\"$\\cos \\theta_\\text{reco}$\")\n",
    "    plt.tight_layout()\n",
    "\n",
    "    fig.subplots_adjust(right=0.95)\n",
    "    cbar = fig.colorbar(phist, ax=axs)\n",
    "    if cmap == \"RdBu_r\": cbar.set_label(\"Percent change\")\n",
    "    else:                cbar.set_label(\"Count\")\n",
    "\n",
    "    plt.show()    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "BESTFIT_SYSTEMATICS = { # From Table IV in the paper.\n",
    "    \"dom_eff\"          :  1.06,\n",
    "    \"hole_ice_p0\"      : -0.27,\n",
    "    \"hole_ice_p1\"      : -0.04,\n",
    "    \"bulk_ice_abs\"     :  0.97,\n",
    "    \"bulk_ice_scatter\" :  0.99,\n",
    "}\n",
    "\n",
    "# Prepare some test parameters for the oscillated flux.\n",
    "TEST_AMP    = 1.\n",
    "TEST_DM31   = 2.41e-3\n",
    "TEST_MUFRAC = 0.05\n",
    "mcnu[\"osc_flux\"] = osc_flux(mcnu, TEST_AMP, TEST_DM31)\n",
    "\n",
    "# Make MC neutrino histograms.\n",
    "hist_nom = make_mcnu_hist(mcnu, dhs, TEST_DM31, NOMINAL_SYSTEMATICS)\n",
    "hist_bf  = make_mcnu_hist(mcnu, dhs, TEST_DM31, BESTFIT_SYSTEMATICS)\n",
    "\n",
    "# Plot.\n",
    "plot_hist(100 * (hist_bf - hist_nom) / hist_nom, cmap=\"RdBu_r\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "Make data and MC muon histograms, then plot them for visual inspection. As we can expect, the data histogram on the top reproduces Figure 17 from the paper exactly.\n",
    "\n",
    "Please note that in this example we are not accounting for many of the systematics in Table IV, which should explain the difference in scale between data events and MC."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data histogram - we won't change this.\n",
    "h_data, _ = np.histogramdd(\n",
    "    [data[\"reco_energy\"], data[\"reco_coszen\"], data[\"pid\"]],\n",
    "    bins = list(BINS.values()), weights = data[\"count\"]\n",
    ")\n",
    "\n",
    "# MC neutrino histogram with systematics in best fit.\n",
    "h_mcnu = make_mcnu_hist(mcnu, dhs, TEST_DM31, BESTFIT_SYSTEMATICS)\n",
    "\n",
    "# MC muon histogram - in this example, we won't change the shape of the muon\n",
    "#     template, but the overall muon rate is scaled via a normalization factor.\n",
    "#     This corresponds to the \"Atmospheric muon scale\" systematic in the paper.\n",
    "#     The best fit value in the paper is 1.39 for this parameter, but we keep it\n",
    "#     free in this simple fit.\n",
    "h_mcmu, _ = np.histogramdd(\n",
    "    [mcmu[\"reco_energy\"], mcmu[\"reco_coszen\"], mcmu[\"pid\"]],\n",
    "    bins = list(BINS.values()), weights = mcmu[\"count\"]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data events (sum: 21914)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MC events (sum: 21830)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Data events (sum: %d)\" % h_data.sum())\n",
    "plot_hist(h_data)\n",
    "\n",
    "print(\"MC events (sum: %d)\" % (h_mcnu.sum() + h_mcmu.sum()))\n",
    "plot_hist(h_mcnu + h_mcmu)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "Prepare functions necessary for the analysis, like reweighting the MC neutrino histogram with different physics parameters and a modified $\\chi^2$ measure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def eval_weights(\n",
    "    mcnu: pd.DataFrame,\n",
    "    h_data: np.ndarray[HIST_SHAPE, float],\n",
    "    h_mcmu: np.ndarray[HIST_SHAPE, float],\n",
    "    dhs: dict[str, pd.DataFrame],\n",
    "    amp: float, dm31: float, mu_frac: float\n",
    ") -> tuple[np.ndarray[HIST_SHAPE, float], np.ndarray[HIST_SHAPE, float]]:\n",
    "    \"\"\"\n",
    "    Makes the MC neutrino histogram re-evaluating the weights based on an oscil-\n",
    "    lated flux evaluated from physics parameters. Then, returns both the MC neu-\n",
    "    trino and MC muon histograms rescaled by a global factor.\n",
    "\n",
    "    Args:\n",
    "        mcnu    : pandas dataframe with MC neutrino data.\n",
    "        h_data  : numpy array with 3D data histogram.\n",
    "        h_mcmu  : numpy array with 3D MC muon histogram.\n",
    "        dhs     : dictionary with hypersurface for each dm31 value.\n",
    "        amp     : real value for the sin2(2 * theta23) in the 2-flavor approx-\n",
    "                  imation.\n",
    "        dm31    : neutrino mass splitting in eV^2.\n",
    "        mu_frac : fraction of the sample composed of atmospheric muon back-\n",
    "                  ground.\n",
    "        returns : MC neutrino and muon histograms, scaled by mu_frac.\n",
    "    \"\"\"\n",
    "    # Get oscillated weight.\n",
    "    mcnu[\"osc_flux\"] = osc_flux(mcnu, amp, dm31)\n",
    "\n",
    "    # Make the MC neutrino histogram.\n",
    "    h_mcnu = make_mcnu_hist(mcnu, dhs, dm31, BESTFIT_SYSTEMATICS)\n",
    "    \n",
    "    # Scale the number of nu events to match the data.\n",
    "    nu_scale = h_data.sum() * (1 - mu_frac) / h_mcnu.sum()\n",
    "    mu_scale = h_data.sum() * mu_frac       / h_mcmu.sum()\n",
    "\n",
    "    return nu_scale * h_mcnu, mu_scale * h_mcmu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def draw_1Dplots(\n",
    "    h_data: np.ndarray[HIST_SHAPE, float], h_mc: np.ndarray[HIST_SHAPE, float]\n",
    "):\n",
    "    \"\"\"\n",
    "    Draws a set of 1D plots to compare the expectation from MC neutrinos and\n",
    "    muons with the data.\n",
    "\n",
    "    Args:\n",
    "        h_data : numpy array with the 3D data histogram.\n",
    "        h_mc   : numpy array with the 3D MC neutrino and muon histograms added\n",
    "                 together.\n",
    "    \"\"\"\n",
    "    BE, BC, BP = BINS[\"reco_energy\"], BINS[\"reco_coszen\"], BINS[\"pid\"]\n",
    "    NROWS, NCOLS = len(BE)-1, len(BP)-1\n",
    "    fig, axs = plt.subplots(\n",
    "        nrows = NROWS, ncols = NCOLS, figsize = (7,12), sharex = True\n",
    "    )\n",
    "\n",
    "    for ci in range(NCOLS):\n",
    "        for ri in range(NROWS):\n",
    "            # Draw expectation.\n",
    "            bc = np.append(h_mc[ri,:,ci], np.array([h_mc[ri,-1,ci]]))\n",
    "            axs[ri][ci].fill_between(\n",
    "                BC, bc-np.sqrt(bc), bc+np.sqrt(bc), color=\"coral\", step=\"post\",\n",
    "                label=\"Expectation\"\n",
    "            )\n",
    "\n",
    "            # Draw data.\n",
    "            axs[ri][ci].stairs(h_data[ri,:,ci], BC, color=\"k\", label=\"Data\")\n",
    "\n",
    "    axs[0][0].set_title(\"Mixed\")\n",
    "    axs[0][1].set_title(\"Tracks\")\n",
    "    for ci in range(NCOLS):\n",
    "        axs[NROWS-1][ci].set_xlabel(r\"$\\cos(\\theta_\\text{reco})$\")\n",
    "    for ri in range(NROWS):\n",
    "        axs[ri][0].set_ylabel(\"Count\")\n",
    "        axs[ri][0].text(\n",
    "            0.3, 0.1, \"E = [%4.1f, %4.1f] GeV\" % (BE[ri], BE[ri+1]),\n",
    "            transform = axs[ri][0].transAxes\n",
    "        )\n",
    "\n",
    "    axs[0][1].legend()\n",
    "    plt.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def chi2(params: list[float]) -> float:\n",
    "    \"\"\"\n",
    "    Calculates a simplified chi2. For simplicity, this function assumes that\n",
    "    `mcnu`, `h_data`, and `h_mcmu` are defined outside the scope of this func-\n",
    "    tion.\n",
    "\n",
    "    Args:\n",
    "        params  : list containing the amplitude, dm31, and muon fraction.\n",
    "        returns : the calculated chi2.\n",
    "    \"\"\"\n",
    "    amp     = params[0]\n",
    "    dm31    = params[1]\n",
    "    mu_frac = params[2]\n",
    "\n",
    "    # Get MC histogram.\n",
    "    h_mcnu_s, h_mcmu_s = eval_weights(\n",
    "        mcnu, h_data, h_mcmu, dhs, amp, dm31, mu_frac\n",
    "    )\n",
    "    mc_total = h_mcnu_s + h_mcmu_s\n",
    "\n",
    "    # Compute and return chi2.\n",
    "    chi2 = (h_data - mc_total)**2 / mc_total\n",
    "    chi2[np.isnan(chi2)] = 0.\n",
    "    return chi2.sum() / (len(params) * np.sqrt(np.sum(h_data)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "Run the fit and plot results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "amp      = 0.998263\n",
      "dm31^2   = 0.002285\n",
      "mu_scale = 0.042960\n",
      "chi2     = 0.812053 (dof: 3)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x1200 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dm31_bins = dhs[\"hs_numu_cc\"][\"deltam31\"].unique()\n",
    "BOUNDS = [\n",
    "    (None, None), (dm31_bins.min() + 1e-5, dm31_bins.max() - 1e-5), (0., 1.)\n",
    "]\n",
    "result = optimize.minimize(\n",
    "    chi2, [TEST_AMP, TEST_DM31, TEST_MUFRAC], method=\"L-BFGS-B\", bounds=BOUNDS\n",
    ")\n",
    "print(\"amp      = %f\" % result.x[0])\n",
    "print(\"dm31^2   = %f\" % result.x[1])\n",
    "print(\"mu_scale = %f\" % result.x[2])\n",
    "print(\"chi2     = %f (dof: %d)\" % (chi2(result.x), len(result.x)))\n",
    "\n",
    "# Plot the result.\n",
    "h_mcnu_s, h_mcmu_s = eval_weights(mcnu, h_data, h_mcmu, dhs, *result.x)\n",
    "h_mc = h_mcnu_s + h_mcmu_s\n",
    "draw_1Dplots(h_data, h_mc)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
